Continuing genetic improvement and biases in genetic gain estimates revealed in historical UK variety trials data

Context: The current pace of yield increase for major crops is not fast enough to meet future demand. Crop breeding programmes are under increasing pressure to improve existing crops further. Quantifying the contribution of these programmes to observed yield increases is important for evaluating their success and identifying if crop improvement goals are likely to be met.Objective: In this paper we explore methods to study the genetic gain of two cereal species, wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). Specifically, the objective of this research was to identify sources of bias in genetic gain estimates of UK variety trials data.Methods: Genetic gain was estimated for fungicide-treated and untreated UK winter wheat, winter barley and spring barley for 1982-2018 using UK National List and Recommended List variety trials data. Subsets of the winter wheat variety trials dataset were used to replicate shorter breeding cycles to quantify the impact of the number and choice of long-term check varieties on estimating genetic gain.Results: While genetic and non-genetic contributions to changes in UK cereal performance are in line with previous estimates, we were able to identify previously undetected changes and biases in estimates of variety performance. Specifically, we observed an increasing yield difference between fungicide treated and untreated variety trials as varieties age, driven by both a breakdown in disease resistance and a previously unobserved long-term increase in yield as varieties age in treated trials. This shows that yields of long-term check varieties cannot be assumed to be stable over time. We found that genetic gain estimates were highly sensitive to the longterm check varieties chosen, whilst the inclusion of multiple checks decreased the standard error of the estimate.Conclusion: The estimation of genetic gain is highly susceptible to bias. We provide recommendations on how to reduce the risk of bias for estimating genetic gain. Implications: Accounting for sources of bias in genetic gain calculations is important in any programme of selection to prevent inaccurate quantification of yield progress.